Tag Archive for: loudspeakers

Enhanced Perceptual Rub & Buzz Measurement for Testing Automotive Loudspeakers

Loudspeaker Rub & Buzz faults are a problem for automotive manufacturers as they sound harsh and immediately give the perception of poor quality. There are two places such faults can occur – during speaker manufacturing and installation of the speaker in the car. A buzzing loudspeaker in a car is disappointing to a customer and is costly to replace. It is also challenging for a service center to determine exactly where the buzzing is coming from and whether it is caused by a faulty loudspeaker or bad installation. Perceptual distortion measurements are often considered the holy grail of end-of-line testing because rejecting speakers with only audible faults increases yield. Although such measurements have been around since 2011, production line adoption has been slow because until now, sensitivity to background noise has made limit-setting challenging. In this paper, a new algorithm is introduced that uses advanced technology to reduce the impact of background noise on the measurement and offer more repeatable results. This facilitates limit setting on the production line and makes it a truly viable production line metric for increasing yield. This same metric may also be used for end-of-line automotive quality control tests. Results from various algorithms will be shown, and their correlation to subjective and other non-perceptual distortion metrics explained.

Author: Steve Temme, Listen, Inc.
Presented at 2022 AES Automotive Conference, Dearborn, MI

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Introduction

The automotive industry’s stringent quality expectations make end-of-line quality testing on automotive speakers and drivers absolutely critical. End-of-line tests typically measure a range of parameters including frequency response, THD, and polarity. Manufacturing-introduced defects such as Rub & Buzz and Loose Particles are also measured. Reliable, automated testing has been available for decades now, and most large manufacturers rely on these software-based systems for identification and rejection of defective products. While these tests do an excellent job of identifying defective units, there is always a certain level of false rejection where units with some distortion fail even though it is completely inaudible to the human ear. From a manufacturing perspective, higher yields and therefore greater profitability is always desirable.

Perceptual Distortion Measurements

This has driven the development of perceptual distortion measurements – automated measurements that replicate the human hearing to detect only audible distortion defects. Such metrics increase production line yield by passing products with inaudible distortion, as the product will still sound exactly as the manufacturer intended. Perceptual methods are very simple to configure for production line use. Since they return a result in Phons, an absolute measurement that can be easily correlated to the listener’s threshold of hearing, the operator can set a fixed limit across the board, regardless of product. Naturally, the price point and quality expectations for the product may influence the level of distortion that is deemed acceptable.

Perceptual Distortion Algorithms

Our algorithm, introduced in 2011, was the first commercial perceptual distortion metric, although in the past couple of years, other test system manufacturers have also started to offer perceptual distortion tests. It offers excellent correlation with human hearing and performs well in laboratory tests. However, like the human ear, repeatability decreases in the presence of background noise. This is not a failure of the algorithm as such, but an indication that the algorithm performs just like a human listener; when background noise is high, audible distortion is masked. This limitation restricts the value of such algorithms on the production line, as with today’s high-volume manufacturing, there is only time for one fast test sweep. If this sweep gets a different result under changing background noise conditions, limit setting becomes challenging, and repeatability and reliability is decreased. Similar algorithms from other test system manufacturers also suffer from the same problems.

New Perceptual Distortion Algorithm Development

This paper details efforts to create an algorithm that hears like a human in quiet conditions, e.g. in a living room or passenger automotive cabin, under the less-than-perfect conditions of a manufacturing environment where considerable and varying background noise may be present. In other words, a perceptual model that is more independent and reliable than the human ear when it comes to noisy environments. The resulting new algorithm overcomes these limitations to offer repeatable end-of-line test results, even in noisy environments. It incorporates noise reduction techniques and enhanced perceptual filters to overcome the reliability and high frequency masking issues of earlier versions. In short, the algorithm offers the performance of an ‘enhanced’ human ear – it detects distortion like an ear in a quiet environment, even when there is background noise. This makes it a viable solution for production line use.

In this paper we explain how the algorithm works, demonstrate how the results compare with earlier perceptual algorithms and show its correlation with human hearing and conventional distortion algorithms. We also compare its performance in the presence of background noise to other perceptual algorithms by adding recorded factory background noise to the signal before passing it through the algorithms.

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More about Listen’s enhanced Perceptual Rub & Buzz algorithm

More about in-car measurement of  impulsive distortion / Buzz, Squeak and Rattle.

End of Line Distortion Measurements

Steve Temme discusses the importance of detecting manufacturing-induced defects such as Rub & Buzz and Loose Particles during end-of-line testing, and explains the various algorithms that are used. He compares conventional and perceptual metrics for the measurement of Rub & Buzz, including Listen’s new enhanced Perceptual Rub & Buzz algorithm, and discusses why it can be beneficial to use both conventional and perceptual measurements in tandem.

Full Article

Evaluation of audio test methods and measurements for end-of-line loudspeaker quality control

In order to minimize costly warranty repairs, loudspeaker OEMS impose tight specifications and a “total quality” requirement on their part suppliers. At the same time, they also require low prices. This makes it important for driver manufacturers and contract manufacturers to work with their OEM customers to define reasonable specifications and tolerances. They must understand both how the loudspeaker OEMS are testing as part of their incoming QC and also how to implement their own end-of-line measurements to ensure correlation between the two.

Authors: Steve Temme, Listen, Inc. and Viktor Dobos, Harman/Becker Automotive Systems Kft.
Presented at ISEAT 2017, Shenzhen, China

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In-Vehicle Distortion Measurement

In-vehicle loudspeaker measurements and distortion audibility articleAuthors: Zarina Bhimani, Steve F. Temme (Listen, Inc.), Patrick Dennis (Nissan Motor Co.).  Reprinted from the 2017 Loudspeaker Industry Sourcebook.

Steve Temme and Patrick Dennis discuss their research exploring test methods that help determine audible distortion and enable manufacturers to test sound equipment after it is installed. Configurations for measuring in-car audio are shown. Objective measurements are made and correlated with subjective analysis, and conclusions drawn as to the level at which music sounds distorted.

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Abstract

Although most automotive speaker manufacturers carry out thorough end-of-line (EOL) driver testing (in many cases, 100% of product), many automotive manufacturers do not test the speakers once they are installed. It is possible for a speaker to develop a fault through damage in transit, handling, or installation. Furthermore, the simple act of installing a loudspeaker into a car can result in vibration issues caused by mounting and other components in the car. Such issues can prove costly for automotive manufacturers. It is not uncommon for a car dealer to install a new set of speakers in a car if a customer complains about sound quality issues. It is, therefore, advisable for automotive manufacturers to invest in both incoming speaker QC and complete EOL testing of installed systems.

Automotive Audio Test Equipment

The test equipment for incoming QC and in-vehicle testing is similar to EOL production tests. In fact the test setup for incoming QC is practically identical to that used in driver manufacturing facilities worldwide. This simple setup consists of an amplifier to drive the speaker, a measurement microphone, and software to measure frequency response, distortion (particularly Rub & Buzz), and polarity. In-vehicle testing is implemented with similar equipment, but the setup differs in that the audio signal is transmitted from the measurement software via an audio interface to the auxiliary, Bluetooth, or USB input to the head unit.

The test signal is played through the speakers, and the signal is picked up by a centrally positioned microphone. Care must be taken in positioning the microphone to ensure that the path from speaker to microphone is not blocked by seats or other parts of the car’s interior. Usually the best position is on, or suspended above, the front seat arm rest.

A single measurement of frequency response and Rub & Buzz is usually sufficient to ensure that the audio profile measured in the car meets specifications. If there are discrepancies, each speaker can then be measured independently (including additional measurements such as polarity) to help identify the cause. Any microphones in the car (e.g., part of a voice control/ telematics system) can also be tested using the same equipment and the car’s own speaker to play the test signal).

A similar test setup can be used for R&D testing (e.g., for voicing the audio system to the car). This might include speaker positioning and equalization of the system for correct tonal and spatial balance including left/right (L/R) and front/back balancing. It may also be used for microphone positioning and directivity measurements and noise cancellation performance.

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More about SoundCheck for Automotive Audio Measurement

More about automotive distortion (Buzz, Squeak and Rattle, Impulsive Distortion) measurement.

AES Technical Committee on Automotive Audio

New Speaker Testing Brochure

lisBrochureSpeakers.inddA new 4 page brochure outlines Listen’s capabilities in loudspeaker and  microspeaker testing. SoundCheck offers simple, fast and accurate testing of any loudspeaker – no matter what the form  factor, functionality, connector or additional features. It measures a full range of driver parameters via traditional analog or digital, wireless/Bluetooth, Toslink, HDMI or USB connections, as well as microphone performance, surround sound and more.
Measurements that SoundCheck can make include:

  • Frequency response
  • Phase & polarity
  • Distortion: harmonic, IM, multitone & non-coherent
  • Rub & Buzz (inc. Perceptual Rub & Buzz)
  • Impedance
  • Directivity (including polar plots)
  • Max SPL
  • Power rating tests
  • Simulated free field measurements
  • Thiele-Small parameters
  • Time-frequency analysis

Download brochure    download_pdf_icon

Measurement of Harmonic Distortion Audibility Using A Simplified Psychoacoustic Model

A perceptual method is proposed for measuring harmonic distortion audibility. This method is similar to the CLEAR (Cepstral Loudness Enhanced Algorithm for Rub & buzz) algorithm previously proposed by the authors as a means of detecting audible Rub & Buzz which is an extreme type of distortion[1,2]. Both methods are based on the Perceptual Evaluation of Audio Quality (PEAQ) standard[3]. In the present work, in order to estimate the audibility of regular harmonic distortion, additional psychoacoustic variables are added to the CLEAR algorithm. These variables are then combined using an artificial neural network approach to derive a metric that is indicative of the overall audible harmonic distortion. Experimental results on headphones are presented to justify the accuracy of the model.

Authors: Steve Temme, Pascal Brunet and Parastoo Qarabaqi
Presented at the 133rd AES Convention, San Francisco, 2012

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